Database Research in 2019: The Year in Review

In 2019, major conferences and research groups focused on various aspects of database management systems (DBMS). Query processing papers covered voice-based OLAP queries, index selection, and auto-tuning systems like Querc. Other notable works included the SageDB system that uses learned models to pick data structures and algorithms for each sub-component of a DBMS, and LSM Trees with adaptive compaction strategies such as Dostoevsky, LSM-Bush, and Jungle. The trend towards specializing databases for specific workloads continued, along with the application of machine learning techniques to improve database behavior and performance.